Accompanying the rapid development and evolution of electronics, computer, network and communication technologies, image processing has been widely used in various multimedia services. Therefore, encoding and compression technologies of image data have become research areas of great importance.
Shapiro proposed the idea called Embedded Zerotree Wavelet (EZW) for image encoding in 1993, since then it has become one of the core technologies for general image encoder, and EZW technique also plays an important role in JPEG-2000.
In the method of signal decomposition used in JPEG-2000, signals are first decomposed in the parallel direction, then in the vertical direction, so that images are decomposed in two-dimension space. For example, the original image size is N×M, the image is first decomposed in the parallel direction, that is, each row of the image is passed through a filter for a down-sampling process to reduce repetition. The image size is reduced to N×M/2. Then, the image is processed in the vertical direction, that is, each column is passed through the filter to obtain an image size of N/2×M/2. Thereupon, the image has passed through two filter levels, obtaining four subband images, as shown in the subband images in FIG. 1 (A). The first subband image passed through two successive low-pass filters is normally denoted by the symbol LL, the second subband image passed through a low-pass filter first then a high-pass filter is normally denoted by the symbol LH, the third subband image passed through a high-pass filter first then a low-pass filter is normally denoted by the symbol HL, and the fourth subband image passed through two successive high pass filters is normally denoted by the symbol HH. Wherein, each subband image can again be passed through a filter for down sampling. Thus, signals are decomposed repeatedly. In FIG. 1 (B) is a graph showing commonly seen image decomposition. The LL subband image is filtered again, thus the block at the most upper left hand side indicates the lowest frequency information (i.e. indicates roughly the waveform of the signals), and the block at the lowest right hand side indicates the highest data information (i.e. indicates finer variations of the waveform). The above-mentioned signal decomposition is obtained by the conventional wavelet transform technique, so no further details will be described herein.
After the above decomposition, the blocks are scanned sequentially starting with the block on the most upper left corner, through the LL subband, HL subband, LH subband and HH subband, to the block at the lowest right corner (as indicated by the direction of the arrow in FIG. 1 (B), which is the result obtained from wavelet transform after three level decomposition), to perform image encoding.
However, when using the level-type image transmission system designed by the EZW technique to transmit images, the result of encoding is not ideal. This is because the encoder designed using the EZW technique encodes according to the resolution of the images. Encoders having different requirements for resolution and encode rate cannot share the bit stream transmitted by the encoder. Although such deficiency could be overcome by simultaneously playing technique, yet independently encoding the same image with different resolutions using this technique would make the common low pass subband being repeatedly encoded and transmitted, thus generating high redundancy.
Among the various image-encoding schemes based on the above EZW, the technique called Set Partition in Hierarchical Tree (SPIHT) proposed by Amir The and Pearlman in 1996 has higher encoding efficiency, even in high compression and low bit rate, the displayed images are still clear. Thus this technique has become the standard of new generation for image compression. Nevertheless, SPIHT technique has to establish three sequences, respectively known as “List of Insignificant Sets” (referred to as LIS hereinafter), “List of Insignificant Pixels” (referred to as LIP hereinafter) and “List of Significant Pixels” (referred to as LSP hereinafter). Therefore, SPIHT requires a large amount of memory for recording, and also raises the requirement for complex hardware computation.
Therefore, there is a need to efficiently save the memory required for image compression encoding to reduce hardware requirement and system computation complexity, while not affecting the resolution of displayed images.